Polipati Ananyachandran, Manikandan Periyasamy, Avuluri Vijaya Bhaskar, M. Helen Santhi, U. Johnson Alengaram, V. Vasugi
The experimental investigation encompassed the development of predictive models for the setting time and compressive strength of high early strength cement mortars (HESCM) incorporating metakaolin (MK), calcium nitrate (Ca(NO 3 ) 2 ), and triethanolamine (C 6 H 15 NO 3 ) at 1, 3, 7, and 28 days intervals using artificial neural networks (ANN). A total of 63 mix combinations were prepared, varying the ratios of MK (5, 10, and 15% replacement of cement), Ca(NO 3 ) 2 , and C 6 H 15 NO 3 . The ANN models were configured with three parameters: the MK replacement ratio, the Ca(NO 3 ) 2 ratio, and the C 6 H 15 NO 3 ratio. Furthermore, the characterization and microstructural examination of HESCM were performed using scanning electron microscopy (SEM) coupled with energy dispersive X- ray spectroscopy (EDX) and X- ray diffraction (XRD) analysis under acrylic resin based chemical curing. The results from experimental and training phases indicate that the ANN system exhibits significant potential in predicting the setting time and compressive strength of HESCM incorporating MK, Calcium Nitrate, and Triethanolamine.